Companies are collecting more data than ever. But, given how difficult it is to unify the many internal and external data streams they’ve built, more data doesn’t necessarily translate into better analytics. The real challenge is to provide deep and broad access to “a single source of truth” in their data that the typically slow ETL process for data warehousing cannot achieve. More than just fast access, analysts need the ability to explore data at a granular level.
In this O’Reilly report, author Courtney Webster presents a roadmap to data centralization that will help your organization make data accessible, flexible, and actionable. Building a genuine data-driven culture depends on your company’s ability to quickly act upon new findings. This report explains how.
- Identify stakeholders: build a culture of trust and awareness among decision makers, data analysts, and quality management
- Create a data plan: define your needs, specify your metrics, identify data sources, and standardize metric definitions
- Centralize the data: evaluate each data source for existing common fields and, if you can, minor variances, and standardize data references
- Find the right tool(s) for the job: choose from legacy architecture tools, managed and cloud-only services, and data visualization or data exploration platforms
Courtney Webster is a reformed chemist in the Washington, D.C. metro area. She spent a few years after grad school programming robots to do chemistry and is now managing web and mobile applications for clinical research trials.
Table of contents
1. Integrated Analytics: Platforms and Principles for Centralizing Your Data
- Building a Data-Driven Culture
- Roadmap to Data Centralization
- Title: Integrated Analytics
- Release date: February 2016
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781491952719
You might also like
Practical Statistics for Data Scientists, 2nd Edition
Statistical methods are a key part of data science, yet few data scientists have formal statistical …
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. …
For years, organizations have struggled to make sense out of their data. IT projects designed to …
The Self-Service Data Roadmap
Data-driven insights are a key competitive advantage for any industry today, but deriving insights from raw …